Simulating Gaussian vectors via randomized dimension reduction and PCA
Statistics Theory
2023-04-18 v1 Computational Finance
Computation
Statistics Theory
Abstract
We study the problem of estimating E(g(X)), where g is a real-valued function of d variables and X is a d-dimensional Gaussian vector with a given covariance matrix. We present a new unbiased estimator for E(g(X)) that combines the randomized dimension reduction technique with principal components analysis. Under suitable conditions, we prove that our algorithm outperforms the standard Monte Carlo method by a factor of order d.
Cite
@article{arxiv.2304.07377,
title = {Simulating Gaussian vectors via randomized dimension reduction and PCA},
author = {Nabil Kahale},
journal= {arXiv preprint arXiv:2304.07377},
year = {2023}
}
Comments
9 pages